




"""simple docstring"""




import json
import sys



def        a__					(							snake_case__			,					snake_case__							)		->   int:
			with open(snake_case__			,					encoding="""utf-8"""							) as f:
						lowerCamelCase      =   json.load(snake_case__							)

			lowerCamelCase      =   ["""<details>""", """<summary>Show updated benchmarks!</summary>""", """ """]

			for benchmark_name in sorted(snake_case__							):
						lowerCamelCase      =   results[benchmark_name]

						lowerCamelCase      =   benchmark_name.split("""/"""							)[-1]
						output_md.append(F'### Benchmark: {benchmark_file_name}'							)

						lowerCamelCase      =   """| metric |"""
						lowerCamelCase      =   """|--------|"""
						lowerCamelCase      =   """| new / old (diff) |"""
						for metric_name in sorted(snake_case__							):
									lowerCamelCase      =   benchmark_res[metric_name]
									lowerCamelCase      =   metric_vals["""new"""]
									lowerCamelCase      =   metric_vals.get("""old"""			,					snake_case__							)
									lowerCamelCase      =   metric_vals.get("""diff"""			,					snake_case__							)

									lowerCamelCase      =   F' {new_val:f}' if isinstance(snake_case__			,					(int, float)							) else """None"""

									if old_val is not None:
												val_str += F' / {old_val:f}' if isinstance(snake_case__			,					(int, float)							) else "None"
									if dif_val is not None:
												val_str += F' ({dif_val:f})' if isinstance(snake_case__			,					(int, float)							) else "None"

									title += " " + metric_name + " |"
									lines += "---|"
									value += val_str + " |"

						output_md += [title, lines, value, " "]

			output_md.append("""</details>"""							)

			with open(snake_case__			,					"""w"""			,					encoding="""utf-8"""							) as f:
						f.writelines("""\n""".join(snake_case__							)							)


if __name__ == "__main__":
					lowerCAmelCase				:     int					=				sys.argv[1]
					lowerCAmelCase				:     str					=				sys.argv[2]

					format_json_to_md(input_json_file, output_md_file)









"""simple docstring"""




from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map



def        a__					(							snake_case__							)		->   Dict[str, torch.Tensor]:
			lowerCamelCase      =   []
			lowerCamelCase      =   []
			lowerCamelCase      =   []

			for rt in rc.restypes:
						lowerCamelCase      =   rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
						restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]							)
						lowerCamelCase      =   {name: i for i, name in enumerate(snake_case__							)}
						restype_atomaa_to_atomaa_list.append(
						    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]							)

						restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]							)

			# Add dummy mapping for restype 'UNK'
			restype_atomaa_to_atomaa_list.append([0] * 14							)
			restype_atomaa_to_atomaa_list.append([0] * 37							)
			restype_atomaa_mask_list.append([0.0] * 14							)

			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.intaa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.intaa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.floataa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   protein["""aatype"""].to(torch.long							)

			# create the mapping for (residx, atom14) --> atom37, i.e. an array
			# with shape (num_res, 14) containing the atom37 indices for this protein
			lowerCamelCase      =   restype_atomaa_to_atomaa[protein_aatype]
			lowerCamelCase      =   restype_atomaa_mask[protein_aatype]

			lowerCamelCase      =   residx_atomaa_mask
			lowerCamelCase      =   residx_atomaa_to_atomaa.long()

			# create the gather indices for mapping back
			lowerCamelCase      =   restype_atomaa_to_atomaa[protein_aatype]
			lowerCamelCase      =   residx_atomaa_to_atomaa.long()

			# create the corresponding mask
			lowerCamelCase      =   torch.zeros([21, 37]			,					dtype=torch.floataa			,					device=protein["""aatype"""].device							)
			for restype, restype_letter in enumerate(rc.restypes							):
						lowerCamelCase      =   rc.restype_atoa[restype_letter]
						lowerCamelCase      =   rc.residue_atoms[restype_name]
						for atom_name in atom_names:
									lowerCamelCase      =   rc.atom_order[atom_name]
									lowerCamelCase      =   1

			lowerCamelCase      =   restype_atomaa_mask[protein_aatype]
			lowerCamelCase      =   residx_atomaa_mask

			return protein



def        a__					(							snake_case__							)		->   Dict[str, np.ndarray]:
			lowerCamelCase      =   tree_map(lambda snake_case__							: torch.tensor(snake_case__			,					device=batch["""aatype"""].device							)			,					snake_case__			,					np.ndarray							)
			lowerCamelCase      =   tensor_tree_map(lambda snake_case__							: np.array(snake_case__							)			,					make_atomaa_masks(snake_case__							)							)
			return out



